MSHI-Mamba: A Multi-Stage Hierarchical Interaction Model for 3D Point Clouds Based on Mamba

Mamba, based on the state space model (SSM), offers an efficient alternative to the quadratic complexity of attention, showing promise for long-sequence data processing and global modeling in 3D object detection. However, applying it to this domain presents specific challenges: traditional serialization methods can compromise the spatial structure of 3D data, and the standard single-layer SSM design may limit cross-layer feature extraction. To address these issues, this paper proposes MSHI-Mamba, a Mamba-based multi-stage hierarchical interaction architecture for 3D backbone networks. We introduce a cross-layer complementary cross-attention module (C3AM) to mitigate feature redundancy in cross-layer encoding, as well as a bi-shift scanning strategy (BSS) that uses hybrid space-filling curves with shift scanning to better preserve spatial continuity and expand the receptive field during serialization. We also develop a voxel densifying downsampling module (VD-DS) to enhance local spatial information and foreground feature density. Experimental results obtained on the KITTI and nuScenes datasets demonstrate that our approach achieves competitive performance, with a 4.2% improvement in the mAP on KITTI, validating the effectiveness of the proposed components.